Machine Learning Engineer (AI/ML Lab)

Job Overview

Changi Airport Group is looking for an experienced Machine Learning Engineer to create and implement advanced AI and machine learning solutions at scale. This role centers on producing deployment-ready models, scalable machine learning workflows, and innovative AI functionalities that address business challenges with tangible outcomes.

Primary Responsibilities

• Design, implement, and deploy machine learning models and AI systems for production use.
• Develop and sustain robust ML pipelines that encompass data ingestion, training, evaluation, deployment, and ongoing monitoring.
• Work alongside cross-disciplinary teams to convert business demands into effective AI and machine learning solutions.
• Enhance the performance, scalability, and dependability of models and systems within production settings.
• Apply MLOps principles including continuous integration and deployment, model version management, experiment tracking, and automation of model retraining.
• Continuously track and manage model performance, addressing drift and maintaining system robustness.
• Integrate AI technologies spanning areas such as computer vision, natural language processing, and modern innovations like generative AI or agent-based frameworks when relevant.
• Uphold compliance with data governance, security protocols, and sound engineering standards.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a closely related discipline.
• At least 3 years of professional experience in machine learning engineering, AI engineering, or similar roles.
• Strong abilities in programming and software development practices.
• Hands-on experience with machine learning and contemporary AI/agentic frameworks such as PyTorch, scikit-learn, LangChain, or equivalents.
• Comprehensive understanding of the full machine learning lifecycle: data preparation, model creation, evaluation, deployment, and monitoring.
• Familiarity with software engineering best practices including testing, version control, and CI/CD processes.
• Knowledge across diverse machine learning methodologies within domains such as computer vision, natural language processing, and generative AI.
• Experience with data processing tools and handling extensive data systems.
• Proven capability in deploying machine learning models in live production environments.
• Experience with application deployment via APIs, containerization, and orchestration frameworks.
• Acquaintance with cloud service providers including AWS, Azure, or Google Cloud Platform.